Entropy features for focal EEG and non focal EEG

Arunkumar, N.; Kumar, K. Ram; Venkataraman, Vinay

doi:10.1016/j.jocs.2018.02.002

Cited by 71 publications

(21 citation statements)

References 23 publications

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“…In contrast with previous findings, permutation entropy algorithms in this case failed completely to detect any difference between focal and non-focal signals, while most embedding algorithms showed a statistically significant increase in entropy in non-focal signals (Figures 12-14). This observation is also consistent with the literature where SEn and FEn have shown the same results on the same data set [42].…”

Section: Discussionsupporting

confidence: 93%

Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy

Amarantidis

Abásolo

2019

Entropy

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Biomedical signals are measurable time series that describe a physiological state of a biological system. Entropy algorithms have been previously used to quantify the complexity of biomedical signals, but there is a need to understand the relationship of entropy to signal processing concepts. In this study, ten synthetic signals that represent widely encountered signal structures in the field of signal processing were created to interpret permutation, modified permutation, sample, quadratic sample and fuzzy entropies. Subsequently, the entropy algorithms were applied to two different databases containing electroencephalogram (EEG) signals from epilepsy studies. Transitions from randomness to periodicity were successfully detected in the synthetic signals, while significant differences in EEG signals were observed based on different regions and states of the brain. In addition, using results from one entropy algorithm as features and the k-nearest neighbours algorithm, maximum classification accuracies in the first EEG database ranged from 63% to 73.5%, while these values increased by approximately 20% when using two different entropies as features. For the second database, maximum classification accuracy reached 62.5% using one entropy algorithm, while using two algorithms as features further increased that by 10%. Embedding entropies (sample, quadratic sample and fuzzy entropies) are found to outperform the rest of the algorithms in terms of sensitivity and show greater potential by considering the fine-tuning possibilities they offer. On the other hand, permutation and modified permutation entropies are more consistent across different input parameter values and considerably faster to calculate.

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Section: Discussionsupporting

confidence: 93%

Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy

Amarantidis

Abásolo

2019

Entropy

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“…Numerous studies have attempted to detect epileptic seizures quantitatively using EEGs [2]- [14]. In these studies, EEGs are generally processed by two steps: frequency decomposition and feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures

Furui

Onishi

Takeuchi

et al. 2021

IEEE Trans. Biomed. Eng.

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The detection of epileptic seizures from scalp electroencephalogram (EEG) signals can facilitate early diagnosis and treatment. Previous studies suggested that the Gaussianity of EEG distributions changes depending on the presence or absence of seizures; however, no general EEG signal models can explain such changes in distributions within a unified scheme. Methods: This paper describes the formulation of a stochastic EEG model based on a multivariate scale mixture distribution that can represent changes in non-Gaussianity caused by stochastic fluctuations in EEG. In addition, we propose an EEG analysis method by combining the model with a filter bank and introduce a feature representing the non-Gaussianity latent in each EEG frequency band. Results: We applied the proposed method to multichannel EEG data from twenty patients with focal epilepsy. The results showed a significant increase in the proposed feature during epileptic seizures, particularly in the high-frequency band. The feature calculated in the high-frequency band allowed highly accurate classification of seizure and nonseizure segments [area under the receiver operating characteristic curve (AUC) = 0.881] using only a simple threshold. Conclusion: This paper proposed a multivariate scale mixture distribution-based stochastic EEG model capable of representing non-Gaussianity associated with epileptic seizures. Experiments using simulated and real EEG data demonstrated the validity of the model and its applicability to epileptic seizure detection. Significance: The stochastic fluctuations of EEG quantified by the proposed model can help detect epileptic seizures with high accuracy.

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“…Due to the low complexity of time-frequency calculation, it is more likely to extract features in the frequency domain or the time-frequency domain in the actual research process [9]. In recent years, many entropy estimators have been used to quantify the complexity of EEG signals according to the instability of EEG signals [3], [20], among them, the estimation of differential entropy is equivalent to the logarithmic energy spectrum of a certain frequency band [4]. Wavelet transform can be used to analyze the characteristic, but there are some disadvantages on high frequency and low frequency resolution.…”

Section: Introductionmentioning

confidence: 99%

Four-classes Human Emotion Recognition Via Entropy Characteristic and Random Forest。

Zhang

Min²

2020

ITC

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Human Emotion Recognition is of vital importance to realize human-computer interaction (HCI), while multichannel electroencephalogram (EEG) signals gradually replace other physiological signals and become the main basis of emotional recognition research with the development of brain-computer interface (BCI). However, the accuracy of emotional classification based on EEG signals under video stimulation is not stable, which may be related to the characteristics of EEG signals before receiving stimulation. In this study, we extract the change of Differential Entropy (DE) before and after stimulation based on wavelet packet transform (WPT) to identify individual emotional state. Using the EEG emotion database DEAP, we divide the experimental EEG data in the database equally into 15 sets and extract their differential entropy on the basis of WPT. Then we calculate value of DE change of each separated EEG signal set. Finally, we divide the emotion into four categories in the two-dimensional valence-arousal emotional space by combining it with the integrated algorithm, Random Forest (RF). The simulation results show that the WPT-RF model established by this method greatly improves the recognition rate of EEG signal, with an average classification accuracy of 87.3%. In addition, we use WPT-RF model to train individual subjects, and the classification accuracy reached 97.7%.

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Entropy features for focal EEG and non focal EEG

Cited by 71 publications

References 23 publications

Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy

Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy

Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures

Four-classes Human Emotion Recognition Via Entropy Characteristic and Random Forest。

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